Sarcomatoid urothelial carcinoma (SUC) is a rare and aggressive variant of bladder cancer with limited data guiding prognosis. In this study, we present the first prognostic nomograms in the literature for 3- and 5-year overall survival (OS) and disease-specific survival (DSS), for patients with SUC derived from the surveillance, epidemiology and end results database (SEER).
Patients with SUC were identified by using the ICD-10 topography codes C67.0-C67.9 (bladder cancer), and the morphologic code 8122 (SUC). Patients were randomly divided into a training cohort (TC) and a validation cohort (VC) (7:3 ratio). Variables significantly associated with OS and DSS were identified with multivariate Cox regression and were used to build the nomograms. Harrel's C-statistic with bootstrap resampling and calibration curves were used for internal (TC) and external (VC) validation. Clinical utility of the nomograms was assessed with the decision curve analysis (DCA). Goodness of fit between the nomograms and the AJCC 8th edition staging system was compared with the likelihood ratio test.
A total of 741 patients with SUC were included (507 TC, 234 VC). No statistically significant differences in baseline characteristics were identified between the 2 cohorts. Sex, SEER stage, radical cystectomy and chemotherapy were common variables for the OS and the DSS nomograms with the addition of age in the former. Optimism-corrected C-statistic for the nomograms was 0.68 and 0.67 for OS and DSS respectively. In comparison, C-statistic for AJCC was 0.59 for OS and 0.60 for DSS (P < 0.001). Calibration curves constructed for the nomograms showed appropriate consistency between predicted and actual survival. The nomograms demonstrated optimal clinical utility in the DCA, outperforming the AJCC staging system, by maintaining a higher clinical net benefits than treat all, treat none and AJCC curves, across threshold probabilities.
We present the first prognostic nomograms developed in patients with SUC. Our models demonstrated superior prognostic performance to the AJCC system, by utilizing a set of variables readily available in daily practice and may serve as useful tools for the individualized risk assessment of these patients.
Urologic oncology. 2023 Mar 15 [Epub ahead of print]
Leonidas N Diamantopoulos, Dimitrios Makrakis, Dimitrios Korentzelos, Michail Alevizakos, Jonathan L Wright, Petros Grivas, Vasiliki Bountziouka, Konstantinos Vadikolias, Maria Lambropoulou, Gregory Tripsianis
Department of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA; Department of Medical Statistics, Faculty of Medicine, Democritus University of Thrace, Alexandroupolis, Greece. Electronic address: ., Department of Medicine, Jacobi Medical Center-Albert Einstein College of Medicine, Bronx, NY., Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA., Division of Hematology/Oncology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA., Department of Urology, University of Washington, Seattle, WA., Division of Medical Oncology, Department of Medicine, University of Washington, Fred Hutchinson Cancer Center, Seattle, WA., Division of Medical Biostatistics, Department of Food Science and Nutrition, University of The Aegean, Myrina, Lemnos, Greece., Department of Medical Statistics, Faculty of Medicine, Democritus University of Thrace, Alexandroupolis, Greece; Department of Neurology, Faculty of Medicine, Democritus University of Thrace, Alexandroupolis, Greece., Department of Pathology, Faculty of Medicine, Democritus University of Thrace, Alexandroupolis, Greece., Department of Medical Statistics, Faculty of Medicine, Democritus University of Thrace, Alexandroupolis, Greece. Electronic address: .